[下载]Peter Jaeckel: Monte Carlo Methods in Finance

Trevor

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收藏 2005-09-17

<P><B><FONT size=3><a href="http://www.amazon.com/gp/reader/047149741X/ref=sib_dp_pt/002-9097004-5288068#reader-link" target="_blank" ><IMG src="http://images.amazon.com/images/P/047149741X.01._BO2,204,203,200_PIsitb-dp-500-arrow,TopRight,45,-64_AA240_SH20_SCLZZZZZZZ_.jpg" border=0></A></FONT></B></P>
<P><B><FONT size=3>Monte Carlo Methods in Finance (Hardcover)
</FONT></B>by <a href="http://www.amazon.com/exec/obidos/search-handle-url/index=books&amp;field-author-exact=Peter%20Jaeckel&amp;rank=-relevance%2C%2Bavailability%2C-daterank/002-9097004-5288068" target="_blank" ><FONT color=#003399>Peter Jaeckel</FONT></A></P>
<P><B><FONT color=#cc6600 size=3>Editorial Reviews
</FONT></B></P>
<DIV class=content><B>Book Description</B>
An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios. Ranging from pricing more complex derivatives, such as American and Asian options, to measuring Value at Risk, or modelling complex market dynamics, simulation is the only method general enough to capture the complexity and Monte Carlo simulation is the best pricing and risk management method available.
The book is packed with numerous examples using real world data and is supplied with a CD to aid in the use of the examples.

<B>Book Info</B>
This text adopts a practical flavor throughout, the emphasis being on financial modeling and derivatives pricing. Provides a detailed explanation of the theoretical foundations of the various methods and algorithms presented.
</DIV>
<UL>
<LI>
<DIV class=content><B>Publisher:</B> John Wiley &amp; Sons; Book &amp; CD edition </DIV></LI>
<LI>
<DIV class=content><B>Language:</B> English</DIV></LI>
<LI>
<DIV class=content><B>ISBN:</B> 047149741X </DIV></LI>
<LI>
<DIV class=content>2002</DIV></LI>
<LI>
<DIV class=content>232 Pages, </DIV></LI>
<LI>
<DIV class=content>PDF</DIV></LI>
<LI>
<DIV class=content>24.3MB</DIV></LI></UL>
<P>

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<P>--------------------------------------------------------------------</P>
<P align=center><FONT color=#e6421a size=4>Peter Jaeckel: Monte Carlo Methods in Finance</FONT></P>
<P>(Official, 2002, 232 Pages, DJUV, 5.59MB, Wiley)</P>
<P>[UseMoney=50]

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math2008

2005-9-17 22:30:00

好贵啊！！！

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sunrain1234

2005-9-17 22:38:00

楼主：我正需要这本书，能否便宜点啊！！

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Trevor

2005-9-17 22:48:00

You will find that my 魅力 is -9, for somebody entered my account stole my money and also sold my 魅力, made me broke.

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duoduoduo

2005-9-17 23:14:00

贵啊

好书最多也就是30啊

[此贴子已经被作者于2005-9-17 23:15:22编辑过]

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Trevor

2005-9-17 23:15:00

[下载]IIya M.Sobol.A Primer for the Monte Carlo Method

A Primer for the Monte Carlo Method (Paperback) by Ilya M. Sobol

Book Description The Monte Carlo method is a numerical method of solving mathematical problems through random sampling. As a universal numerical technique, the method became possible only with the advent of computers, and its application continues to expand with each new computer generation. A Primer for the Monte Carlo Method demonstrates how practical problems in science, industry, and trade can be solved using this method. The book features the main schemes of the Monte Carlo method and presents various examples of its application, including queueing, quality and reliability estimations, neutron transport, astrophysics, and numerical analysis. The only prerequisite to using the book is an understanding of elementary calculus.

Product Details

Paperback: 107 pages
Publisher: CRC-Press; 1 edition (May 19, 1994)
Language: English
ISBN: 084938673X

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Trevor

2005-9-17 23:21:00

[推荐]

Monte Carlo Simulation and Finance

by Don L. McLeish

Description of Monte Carlo Simulation and Finance

A state-of-the-art book on Monte Carlo simulation methods for finance professionals and students Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields. Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon.

About Don L. McLeish

Don L. McLeish (Ontario, Canada) is Professor of Statistics and Actuarial Science at the University of Waterloo. His research has focused on statistical models for financial data, including the application of wide-tail alternatives to the normal distribution such as stable processes, and the consequences for derivatives and asset pricing.

290 Pages,

Published by John Wiley & Sons,

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Trevor

2005-9-17 23:23:00

[推荐]

Monte Carlo Methods in Financial Engineering

by Paul Glasserman

Our price: £38.48

Product code: 16497, ISBN: 0387004513, 600 pages, hardback, published by Springer Verlag, 1st edition, 2003

Description of Monte Carlo Methods in Financial Engineering

Monte Carlo Methods are among the most broadly applicable and thus most powerful tools for valuing derivatives securities and measuring their risks. As computer speeds continue to increase and new research expands the scope and efficiency of these methods, their use is destined to grow. This book is devoted to the use of Monte Carlo methods in finance. Advances in Monte Carlo methods in financial engineering take place at the interface between academic research and industry practice. This book targets that interface developing theory closely tied to applications. It is roughly divided into three parts: the first three chapters concentrate on the basics of Monte Carlo methods; the next three develop ways to improve Monte Carlo methods; and the final four chapters deal with more specialized problems arising, in particular applications of Monte Carlo to financial engineering. This book will serve as a reference for practitioners and researchers and will also be suitable as a graduate text for courses on computational finance.

Contents of Monte Carlo Methods in Financial Engineering

1. Foundations Principles of Monte Carlo Principles of Derivatives Pricing 2. Generating Random Numbers and Random Variables Random Number Generation General Sampling Methods Normal Random Variables and Vectors 3. Generating Sample Paths Brownian Motion Gaussian Short Rate Models Square-Root Diffusions Processes with Jump Forward Rate Models: Continuous Rates Forward Rate Models: Simple Rates 4. Variance Reduction Techniques Control Variates Antithetic Variates Stratified Sampling Latin Hypercube Sampling Matching Underlying Assets Importance Sampling Concluding Remarks 5. Quasi-Monte Carlo Methods General Principles Low-Discrepancy Sequences Lattice Rules Randomized QMC The Finance Setting Concluding Remarks 6. Discretization Methods Introduction Second-Order Methods Extensions Extremes and Barrier Crossings: Brownian Interpolation Changing Variables Concluding Remarks 7. Estimating Sensitivities Finite-Difference Approximations Pathwise Derivative Estimates The Likelihood Ratio Method Concluding Remarks 8. Pricing American Options Problem Formulation Parametric Approximations Random Tree Methods State-Space Partitioning Stochastic Mesh Methods Regression-Based Methods and Weights Duality Concluding Remarks 9. Applications in Risk Management Loss Probabilities and Value-at-Risk Variance Reduction Using the Delta-Gamma Approximation A Heavy-Tailed Setting Credit Risk Concluding Remarks A Appendix: Convergence and Confidence Intervals B Appendix: Results from Stochastic Calculus C Appendix: The Term Structure of Interest Rates References Index

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Trevor

2005-9-17 23:27:00

[推荐]

Monte Carlo Statistical Methods

by Christian P. Robert and George Casella

Our price: £50.58 + postage

Description of Monte Carlo Statistical Methods

Until the advent of powerful and accessible computing methods, the experimenter was often confronted with a difficult choice. Either describe an accurate model of a phenomenon, which would usually preclude the computation of explicit answers, or choose a standard model which would allow this computation, but may not be a close representation of a realistic model. This dilemma is present in many branches of statistical applications, for example in electrical engineering, aeronautics, biology, networks, and astronomy. Markov chain Monte Carlo methods have been developed to provide realistic models.

Contents of Monte Carlo Statistical Methods

1 Introduction Statistical Models Likelihood Methods Bayesian Methods Deterministic Numerical Methods Problems Notes 2 Random Variable Generation Basic Methods - Introduction - The Kiss Generator - Beyond Uniform Distributions Transformation Methods Accept-Reject Methods - General Principle - Envelope Accept-Reject Methods - Log-Concave Problems Notes 3 Monte Carlo Integration Introduction Classical Monte Carlo integration Importance Sampling - Principles - Finite Variance Estimators - Comparing Importance Sampling with Accept-Reject Riemann Approximations Laplace Approximations The Saddlepoint Approximation - An Edgeworth Derivation - Tail Areas Acceleration Methods - Antithetic Variables - Control Variates - Conditional Expectations Problems Notes 4 Markov Chains Essentials for MCMC Basic notions Irreducibility, atoms and small sets - Irreducibility - Atoms and small sets - Cycles and Aperiodicity Transience and Recurrence - Classification of irreducible chains - Criteria for Recurrence - Harris Recurrence Invariant Measures Ergodicity and convergence - Ergodicity - Geometric convergence - Uniform ergodicity Limit theorems - Ergodic theorems - Central limit theorems Covariance in Markov Chains Problems Notes 5 Monte Carlo Optimization Introduction Stochastic Exploration - A basic solution - Gradient Methods - Simulated Annealing - Prior Feedback Stochastic Approximation - Missing data models and demarginalization - Monte Carlo Approximation - The EM Algorithm - Monte Carlo EM Problems Notes 6 The Metropolis-Hastings Algorithm Monte Carlo Methods based on Markov Chains The Metropolis-Hastings algorithm - Definition - Convergence Properties A Collection of Metropolis-Hastings Algorithms - The Independent Case - Random Walks - ARMS: A General Metropolis-Hastings Algorithm Optimization and Control - Optimizing and Acceptance Rate - Conditioning and Acceleration Further Topics - Reversible Jumps - Langevin algorithms Problems Notes 7 The Gibbs Sampler General Principles - Definition - Completion - Convergence Properties - Gibbs sampling and Metropolis-Hastings - The Hammersley-Clifford Theorem - Hierarchical Structures The Two-Stage Gibbs Sampler - Dual Probability Structures - Reversible and Interleaving Chains - Monotone Covariance and Rao-Blackwellization - The Duality Principle Hybrid Gibbs samplers - Comparison with Metropolis-Hastings Algorithms - Mixtures and Cycles - Metropolizing the Gibbs sampler - Reparameterization Improper Priors Problems Notes 8 Diagnosing Convergence Stopping the Chain - Convergence Criteria - Multiple Chains - Conclusions Monitoring Convergence to the Stationary Distribution - Graphical Methods - Nonparametric tests of stationarity - Renewal Methods - Distance evaluations Monitoring Convergence of Averages - Graphical Methods - Multiple Estimates - Renewal Theory - Within and between variances Simultaneous Monitoring - Binary control - Valid Discretization Problems Notes 9 Implementation in Missing Data Models Introduction First examples - Discrete Data Models - Data missing at random Finite mixtures of distributions A Reparameterization of Mixtures Extensions - Hidden Markov chains - Changepoint models - Stochastic Volatility Problems Notes A Probability Distributions B Notation Mathematical Probability Distributions Markov Chains Statistics Algorithms C References Author Index Subject Index

[此贴子已经被作者于2005-9-18 3:22:04编辑过]

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zwen

2005-9-17 23:29:00

楼主多贴些Monte Carlo的东西

偶大力支持!

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Trevor

2005-9-17 23:29:00

[推荐]

Monte Carl Methodologies and Applications for Pricing and Risk Management

by Bruno Dupire

Price: £109.00

Product code: 9487, ISBN: 189933291X, 340 pages, paperback, published by Risk Books, 1st edition, 1998

Description of Monte Carl Methodologies and Applications for Pricing and Risk Management

A core reference of classic research and new writing on the methodologies and applications of Monte Carlo simulation - An edited collection of new writing and reference papers structured to provide a unique routemap - Selected and introduced by leading practitioner and theoretician, Bruno Dupire - Covers pricing, Monte Carlo methodologies, yield curves models, fixed income and generalities

Contents of Monte Carl Methodologies and Applications for Pricing and Risk Management

Authors Introduction Part I: Generalities 1. Options: A Monte Carlo Approach 2. Monte Carlo Methods for Security Pricing 3. Monte Carlo Toolkit Part II: Pricing 4. Dimension Reduction and Other Ways of Speeding Monte Carlo Simulation 5. Average Intelligence 6. Beyond Average Intelligence 7. Strata Gems 8. Recovering Identity 9. Greeks in Monte Carlo 10. Quicker on the Curves 11. Exact Exotics 12. Monte Carlo Simulation of Options on Joint Minima and Maxima 13. Model calibration in the Monte Carlo Framework Part III: American Style 14. Valuing American Options in a Path-Simulation Model 15. Numerical valuation of High-Dimensional Multivariate American Securities 16. Monte Carlo Methods for Pricing High-Dimensional American Options: an Overview Pat IV: Fixed Income 17. Pricing Interest Rate Exotics by Monte Carlo Simulation 18. Efficient and Flexible Bond Option Valuation in the Heath, Jarrow and Morton Framework 19. Term Structure Dynamics and Mortgage Valuation Part V: VAR 20. Calculating Value-at-Risk with Monte Carlo Simulation 21. Beyond VAR and Stress Testing 22. Using Non-Normal Monte Carlo Simulation to Compute Value-at-Risk 23. Scrambled Nets for Value-at-Risk Valuation Part VI: Deterministic Methods 24. Quasi-Monte Carlo Methods in Numerical Finance 25. New Methodologies for Valuing Derivatives 26. Valuation of Mortgage-backed Securities Using Brownian Bridges to Reduce Effective Dimension 27. Smoothness and Dimensions Reduction in Quasi-Monte Carlo Methods 28. Beating Monte Carlo 29. Monte Carlo Motoring 30. Laudable Lattices 31. Inelegant Efficiency Glossary of Monte Carlo Terms

[此贴子已经被作者于2005-9-18 1:01:54编辑过]

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pan1111111

2005-9-18 00:06:00

怎么后面的只见介绍不见书啊？

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Trevor

2005-9-18 00:58:00

[推荐]

Simulation and the Monte Carlo Method

This is a classic introduction to the Monte Carlo method. It starts with basic concepts. It illustrates techniques for generating pseudorandom numbers and pseudorandom variates. The discussions of variance reduction techniques are clear and very accessible. The book closes with discussions of some more specialized topics.

This book is perfect for a quantitatively oriented professional who has some intuitive familiarity with the Monte Carlo method but wants to achieve more formal understanding so they can implement variance reduction techniques in their Monte Carlo analyses.

Contents

1. Systems, Models, Simulation and the Monte Carlo Method

2. Random Number Generation

3. Random Variate Generation

4. Monte Carlo Integration and Variance Reduction Techniques

5. Linear Equations and Markov Chains

6. Regenerative Method for Simulation Analysis

7. Monte Carlo Optimization

[此贴子已经被作者于2005-9-18 1:00:35编辑过]

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Trevor

2005-9-18 01:04:00

[Introduction]

Monte Carlo Concepts, Algorithms and Applications

Author:

George S. Fishman

This is the authoritative text on the Monte Carlo method. Fishman covers the standard topics of integral estimation, variance reduction and random number generation. He also includes extensive material on simulating stochastic processes. The treatment is very formal and meticulously detailed. Numerous algorithms are presented, and references are cited extensively. The book is not an easy read. You should read Rubinstein (1981) before attempting Fishman. The book is unsurpassed as a reference.

Contents

Introduction

Estimating Volume and Count

Generating Samples

Increasing Efficiency

Random Tours

Designing and Analyzing Sample Paths

Generating Pseudorandom Numbers

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Trevor

2005-9-18 01:07:00

[Introduction]Random Number Generation and Quasi-Monte Carlo Methods

This is an essential text on quasi-random numbers. Neiderreiter is a leading researcher in the field, and much of the work presented is his own. He covers discrepancy measures, low-discrepancy point sets, nets and (t,s)-sequences, lattices, random number generation, and random vector generation. The material on random number and vector generation is outdated, but the rest of the book represents the foundations of today's quasi-Monte Carlo methods. This is a difficult book to read. Neiderreiter makes extensive use of finite field theory and other branches of mathematics that are not common fare, even for financial engineers. If you are going to work with quasi-random numbers, you should read this book.

Contents

1. Monte Carlo Methods and Quasi-Monte Carlo Methods

Monte Carlo methods

Quasi-Monte Carlo methods

2. Quasi-Monte Carlo Methods for Numerical Integration

Discrepancy

Error bounds

3. Low-Discrepancy Point Sets and Sequences

Classical constructions

General discrepancy bounds

4. Nets and (t,s)-Sequences

Definitions and discrepancy bounds

Combinatorial connections

General construction principles

A special construction of nets

A special construction of (t,s)-sequences

5. Lattice Rules for Numerical Integration

The method of good lattice points

Existence theorems for good lattice points

General lattice rules and their classification

Existence theorems for efficient lattice rules

6. Quasi-Monte Carlo Methods for Optimization

General theory of quasirandom search methods

Low-dispersion point sets and sequences

7. Random Numbers and Pseudorandom Numbers

Random number generation

Pseudorandom numbers

Classical generators

8. Nonlinear Congruential Pseudorandom Numbers

The general nonlinear congruential method

The inversive congruential method

9. Shift-Register Pseudorandom Numbers

The digital multistep method

The generalized feedback shift-register (GFSR) method

10. Pseudorandom Vector Generation

The matrix method

Nonlinear methods

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hkswen

2005-9-18 03:03:00

还值，顶！

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bizarre_74

2005-9-18 08:26:00

哥哥,偶没钱,能否赠送一本,我会努力赚钱,等我有钱了还你

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eijuhz

2005-9-18 09:29:00

后面几本书也请传上来

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solomon

2005-9-18 09:32:00

hehe, 不错，不错，坛子里每天都有新惊喜啊

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duoduoduo

2005-9-18 10:15:00

第一本书和他帖子的介绍不符

原书304页

可是他提供的才235页

希望l楼主解释一下

谢谢

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Trevor

2005-9-18 11:35:00

I have two edition, one Include 版权页, the other is 15.8MB and excludes 版权页 as posted. The introduction here is coped from online and just for your reference. Although it has just 234 page, I found nothing missing. I am sorry for the error. I would like to thank duoduoduo for his suggestion.

[此贴子已经被作者于2005-9-18 11:39:15编辑过]

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Trevor

2005-9-18 22:50:00

[下载]

Under Construction!

[此贴子已经被作者于2005-9-19 11:24:31编辑过]

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zwen

2005-9-18 23:29:00

以下是引用Trevor在2005-9-18 22:50:11的发言：

Peter Jaeckel: Monte Carlo Methods in Finance

(Official, 2002, 232 Pages, DJUV, 5.59MB, Wiley)

偶已经购买了

能否把我加入定员帖里

想下载正式颁的

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duoduoduo

2005-9-18 23:31:00

以下是引用zwen在2005-9-18 23:29:30的发言：

偶已经购买了

能否把我加入定员帖里

想下载正式颁的

是的，我也购买了

而且想打印出来

所以希望可以下载正式版的，谢谢了

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Trevor

2005-9-19 03:55:00

[下载]

Under construction

[此贴子已经被作者于2005-9-19 11:22:12编辑过]

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Trevor

2005-9-19 09:15:00

[求助]

SAS for Monte Carlo Studies: A Guide for Quantitative ResearchersBy Xitao Fan, Akos Felsovalyi, Stephen A. Sivo, and Sean C. Keenan

Book Description

With the advance of computing technology, Monte Carlo simulation research has become increasingly popular among quantitative researchers in a variety of disciplines. More and more, statistical methods are being subjected to rigorous empirical scrutiny in the form of statistical simulation so that their limitations and strengths can be understood. With the combination of powerful built-in statistical procedures and versatile programming capabilities, the SAS System is ideal for conducting Monte Carlo simulation research!

SAS for Monte Carlo Studies: A Guide for Quantitative Researchers provides a detailed and practical guide for conducting Monte Carlo studies using the SAS System. Quantitative researchers will find this book attractive for its practicality and for its many hands-on application examples of Monte Carlo research.

Topics addressed in this book include:

basic procedures for Monte Carlo simulation
generating univariate random numbers in SAS
generating data in Monte Carlo studies
automating Monte Carlo simulations
conducting Monte Carlo studies that involve univariate statistical techniques
conducting Monte Carlo studies for multivariate techniques
examples for Monte Carlo simulation in finance: estimating default risk and Value-at-Risk (VaR)
modeling time series processes with SAS/ETS software.

Book Description

Author Biography

Table of Contents

Contact the Author

Order This Book

[此贴子已经被作者于2005-9-19 9:36:02编辑过]